Multi-label classification:  In multi-label classification, a multi-label classifier predicts multiple labels for an input.
Many deep learning classifiers [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]} have been proposed for multi-label classification.
For instance, a naive method for multi-label classification is to train independent binary classifiers for each label and use ranking or thresholding to derive the final predicted labels. This method, however, ignores the topology structure among labels and thus cannot capture the label co-occurrence dependency (e.g., mouse and keyboard usually appear together). In response, several methods [3]}, [8]} have been proposed to improve the performance of multi-label classification via exploiting the label dependencies in an input.
Despite their effectiveness, these methods rely on complicated architecture modifications. To mitigate the issue, some recent studies [10]}, [11]} proposed to design new loss functions. For instance, Baruch et al. [11]} introduced an asymmetric loss (ASL). Roughly speaking, their method is based on the observation that, in multi-label classification, most inputs contain only a small fraction of the possible candidate labels, which leads to under-emphasizing gradients from positive labels during training. Their experimental results indicate that their method achieves state-of-the-art performance on multiple benchmark datasets.
